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A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

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Published on: February 8, 2016

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping.

Aceer Nadeem, Sarah Lyons, Aidan Kindopp

Biorxiv : the Preprint Server for Biology

|March 18, 2024

Summary

This summary is machine-generated.

Near-infrared fluorescence spectral fingerprinting of DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) distinguishes macrophage phenotypes with over 95% accuracy. Shorter DNA sequences enhance interaction and model performance for cellular identification.

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Last Updated: Jun 30, 2025

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Area of Science:

Biomedical Engineering
Nanotechnology
Cell Biology

Background:

Spectral fingerprinting identifies compounds and cellular interactions.
Macrophage phenotypes (M1/M2) play crucial roles in immunity and disease.
Engineered nanomaterials offer potential for cellular analysis.

Approach:

Utilized near-infrared (NIR) fluorescence spectral fingerprinting and Raman microscopy.
Investigated interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells.
Employed a support vector machine (SVM) model for phenotype discrimination.

Key Points:

DNA-SWCNT uptake and defect ratios differed significantly between M1 and M2 macrophages.
Intra-endosomal environments created distinct optical features for phenotype identification.
SVM model achieved >95% accuracy in differentiating M1 and M2 macrophages.
Shorter DNA sequences (e.g., GT6) improved model accuracy (>87%) due to enhanced SWCNT-biomolecule interactions.

Conclusions:

NIR spectral fingerprinting coupled with machine learning can accurately identify macrophage phenotypes.
DNA-SWCNT stability and sequence length are critical for effective cell analysis.
This approach holds promise for developing nanomaterial-based platforms for real-time in vivo cellular monitoring.